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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 192 Documents
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A Multivariate LSTM Approach for Monthly Rice Production Forecasting in East Java Firdausi, Hasanur Mohammad; Utomo, Satryo Budi; Rahardi, Gamma Aditya; Prasetiyo, Dani Hari Tunggal
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.595

Abstract

Accurate forecasting of rice output is essential for improving regional food security planning, particularly in East Java Province, which serves as a major national rice granary. This study develops a Long Short-Term Memory (LSTM) model to predict rice production using monthly data on production and harvested area from 2018 to 2024. The methodology includes data preprocessing, normalization, sequence construction with a sliding window, training of a multivariate LSTM model, and performance evaluation using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results show that the LSTM model achieves superior predictive accuracy, with an MAE of 95,030.16, RMSE of 120,229.01, and MAPE of 16.64%, significantly outperforming baseline Moving Average and Linear Regression models. While the model effectively captures seasonal production trends, some inaccuracies remain during periods of anomalous production values. These findings suggest that the LSTM model is effective for projecting rice production and may provide a foundation for early warning systems and regional food distribution strategies. Further improvements could be realized by integrating climate variables or adopting a hybrid model architecture to enhance predictive precision.
WebSocket-Based Smart Surveillance Camera for Real-Time Detection of Occupational Health and Safety PPE Non-Compliance in Industrial Areas Sabarto, Rivaldi Azis; Sulistiyowati, Indah; Syahrorini, Syamsudduha; Wisaksono, Arief
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.597

Abstract

In industrial settings, ensuring adherence to Occupational Health and Safety (OHS) Personal Protective Equipment (PPE) regulations continues to be a crucial challenge. The creation of a WebSocket-based smart surveillance camera system for the real-time identification and reduction of PPE infractions is discussed in the paper. The proposed system includes an ESP32-S3 microcontroller accompanied by an OV5640 camera module, acting as an edge-processing embedded platform. The Edge Impulse machine learning framework was used to train image classification and detection models, enabling efficient low-latency inference directly on the device. A websocket enabled web server streams video frames in real time for constant monitoring, with instant display using regular browsers without wasting bandwidth. Experimental results demonstrate that even with limited computational resources, the system is able to perform on-device inference with very high responsiveness and good detection accuracy. This technology provides a scalable and affordable way to enhance OHS compliance monitoring in industry, reduce reliance on manual supervision, and encourage proactive risk mitigation methodologies.

Page 20 of 20 | Total Record : 192

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